Lightning-AI/pytorch-lightning 0.8.0

Metrics, speed improvements, new hooks and flags

on GitHub

Overview

Highlights of this release are adding Metric package and new hooks and flags to customize your workflow.

Major features:

• brand new Metrics package with built-in DDP support (by @justusschock and @SkafteNicki)
• hparams can now be anything! (call self.save_hyperparameters() to register anything in the _init_
• many speed improvements (how we move data, adjusted some flags & PL now adds 300ms overhead per epoch only!)
• much faster ddp implementation. Old one was renamed ddp_spawn
• better support for Hydra
• added the overfit_batches flag and corrected some bugs with the limit_[train,val,test]_batches flag
• added conda support
• tons of bug fixes 😉

Detail changes

Added

• Added overfit_batches, limit_{val|test}_batches flags (overfit now uses training set for all three) (#2213)
• Added metrics
  • Base classes (#1326, #1877)
  • Sklearn metrics classes (#1327)
  • Native torch metrics (#1488, #2062)
  • docs for all Metrics (#2184, #2209)
  • Regression metrics (#2221)
• Added type hints in Trainer.fit() and Trainer.test() to reflect that also a list of dataloaders can be passed in (#1723)
• Allow dataloaders without sampler field present (#1907)
• Added option save_last to save the model at the end of every epoch in ModelCheckpoint (#1908)
• Early stopping checks on_validation_end (#1458)
• Attribute best_model_path to ModelCheckpoint for storing and later retrieving the path to the best saved model file (#1799)
• Speed up single-core TPU training by loading data using ParallelLoader (#2033)
• Added a model hook transfer_batch_to_device that enables moving custom data structures to the target device (#1756)
• Added black formatter for the code with code-checker on pull (#1610)
• Added back the slow spawn ddp implementation as ddp_spawn (#2115)
• Added loading checkpoints from URLs (#1667)
• Added a callback method on_keyboard_interrupt for handling KeyboardInterrupt events during training (#2134)
• Added a decorator auto_move_data that moves data to the correct device when using the LightningModule for inference (#1905)
• Added ckpt_path option to LightningModule.test(...) to load particular checkpoint (#2190)
• Added setup and teardown hooks for model (#2229)

Changed

• Allow user to select individual TPU core to train on (#1729)
• Removed non-finite values from loss in LRFinder (#1862)
• Allow passing model hyperparameters as complete kwarg list (#1896)
• Renamed ModelCheckpoint's attributes best to best_model_score and kth_best_model to kth_best_model_path (#1799)
• Re-Enable Logger's ImportErrors (#1938)
• Changed the default value of the Trainer argument weights_summary from full to top (#2029)
• Raise an error when lightning replaces an existing sampler (#2020)
• Enabled prepare_data from correct processes - clarify local vs global rank (#2166)
• Remove explicit flush from tensorboard logger (#2126)
• Changed epoch indexing from 1 instead of 0 (#2206)

Deprecated

• Deprecated flags: (#2213)
  • overfit_pct in favour of overfit_batches
  • val_percent_check in favour of limit_val_batches
  • test_percent_check in favour of limit_test_batches
• Deprecated ModelCheckpoint's attributes best and kth_best_model (#1799)
• Dropped official support/testing for older PyTorch versions <1.3 (#1917)

Removed

• Removed unintended Trainer argument progress_bar_callback, the callback should be passed in by Trainer(callbacks=[...]) instead (#1855)
• Removed obsolete self._device in Trainer (#1849)
• Removed deprecated API (#2073)
  • Packages: pytorch_lightning.pt_overrides, pytorch_lightning.root_module
  • Modules: pytorch_lightning.logging.comet_logger, pytorch_lightning.logging.mlflow_logger, pytorch_lightning.logging.test_tube_logger, pytorch_lightning.overrides.override_data_parallel, pytorch_lightning.core.model_saving, pytorch_lightning.core.root_module
  • Trainer arguments: add_row_log_interval, default_save_path, gradient_clip, nb_gpu_nodes, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps
  • Trainer attributes: nb_gpu_nodes, num_gpu_nodes, gradient_clip, max_nb_epochs, min_nb_epochs, nb_sanity_val_steps, default_save_path, tng_tqdm_dic

Fixed

• Run graceful training teardown on interpreter exit (#1631)
• Fixed user warning when apex was used together with learning rate schedulers (#1873)
• Fixed multiple calls of EarlyStopping callback (#1863)
• Fixed an issue with Trainer.from_argparse_args when passing in unknown Trainer args (#1932)
• Fixed bug related to logger not being reset correctly for model after tuner algorithms (#1933)
• Fixed root node resolution for SLURM cluster with dash in hostname (#1954)
• Fixed LearningRateLogger in multi-scheduler setting (#1944)
• Fixed test configuration check and testing (#1804)
• Fixed an issue with Trainer constructor silently ignoring unknown/misspelt arguments (#1820)
• Fixed save_weights_only in ModelCheckpoint (#1780)
• Allow use of same WandbLogger instance for multiple training loops (#2055)
• Fixed an issue with _auto_collect_arguments collecting local variables that are not constructor arguments and not working for signatures that have the instance not named self (#2048)
• Fixed mistake in parameters' grad norm tracking (#2012)
• Fixed CPU and hanging GPU crash (#2118)
• Fixed an issue with the model summary and example_input_array depending on a specific ordering of the submodules in a LightningModule (#1773)
• Fixed Tpu logging (#2230)
• Fixed Pid port + duplicate rank_zero logging (#2140, #2231)

Contributors

@awaelchli, @baldassarreFe, @Borda, @borisdayma, @cuent, @devashishshankar, @ivannz, @j-dsouza, @justusschock, @kepler, @kumuji, @lezwon, @lgvaz, @LoicGrobol, @mateuszpieniak, @maximsch2, @moi90, @rohitgr7, @SkafteNicki, @tullie, @williamFalcon, @yukw777, @ZhaofengWu

If we forgot someone due to not matching commit email with GitHub account, let us know :]